Developing hardware for AI is both costly and technicallydemanding. The software stack evolves rapidly and is often optimized aroundGPUs. These realities raise a fundamental challenge: how can one validatehardware that does not yet exist for emerging AI workloads? Fortunately, mostAI applications are built on a relatively small set of frameworks, notablyPyTorch and JAX, which provide natural integration pathways for newaccelerators. Hooking into the integration layers from software frameworks tohardware offers a practical interface for accelerator simulators to assess bothfunctional correctness and performance characteristics.

Taking inspiration from PyTorch's example acceleratorintegration (OpenReg), this internship effort focuses on simulating acceleratormemory behaviour during distributed LLM deployment. The goal is to enableearly-stage evaluation of memory bottlenecks, communication patterns, andexecution efficiency before hardware is physically available, thereby reducingdesign risk and accelerating the development cycle for novel AI accelerators.

Objectives

develop a simulator for faithfully modelling acceleratormemory behaviour in distributed LLM execution;
integrate the model with existing PyTorchexecution paths to capture realistic memory access patterns;
analyze memory usage, identify potential memorybottlenecks under distributed deployment scenarios;
disseminate the findings by publishing inappropriate conference or journal.

Desirable skills

Familiarity with Python and C++; experience with PyTorchis a plus
Interest in computer architecture; knowledge of architectural simulators suchas gem5 and onnxim is a plus
Basic familiarity with LLM models and distribution strategies

Type of internship: Master internship, PhD internship

Duration: 6 months

Required educational background: Computer Science, Electrotechnics/Electrical Engineering

University promotor: Debjoti Bhattacharjee (KULeuven)

Supervising scientist(s): For further information or for application, please contact Nathan Laubeuf (< E-Mail aus Sicherheitsgründen gelöscht >)

The reference code for this position is 2026-INT-327. Mention this reference code in your application.

Imec allowance will be provided.

Applications should include the following information: